Search Results (177)

Climate change poses significant challenges to the economic and social sustainability of urban dwellers, particularly in the real estate market, where rising temperatures are affecting property values. While most research focuses on how climate change impacts buyers and sellers, this study shifts attention to renters, who may be more vulnerable to climate-induced price increases. By analyzing rental price and climate data, this study uses ordinary least squares (OLS) and fixed-effects regressions to assess the impact of temperature fluctuations on rental rates across 50 major U.S. metropolitan areas. The findings reveal a positive and significant relationship between rising temperatures and rental rates, particularly in the Northeastern and Southern U.S. These results suggest that targeted policy interventions may help ease financial pressures on vulnerable renters and support more sustainable urban development over time. The analysis also highlights the potential role of energy efficiency measures in rental housing to lower energy costs and alleviate rent burdens. Additionally, the findings indicate that local policymakers may consider rent stabilization strategies and investments in urban green infrastructure to protect low-income renters, reduce localized heat exposure, and promote long-term urban resilience. Full article

This study explores the role of egoistic and altruistic values on green real estate purchase intention among young consumers in Canada aged between 20 and 40 years. In addition, this study examines the mediating effects of pro-environmental self-identity between social consumption motivation and green real estate purchase intention. A quantitative cross-sectional research design with an explanatory nature is employed. A total of 432 participating consumers in Canada, comprising 44% men and 48% women, with a graduate educational background accounting for 46.7%, and the ages between 24 and 35 contributing 75.2%, were part of the study, and the data collection used a survey method with a purposive sampling, followed by a respondent-driven method. Descriptive and inferential statistics were performed on the scales used for the study variables. A structural equational model and path analysis were conducted to derive the results, and the relationships were positive and significant. The study results infer the factors contributing to green real estate purchase intention, including altruistic value, egoistic value, social consumption motivation, and pro-environmental self-identity, with pro-environmental self-identity mediating the relationship. This study emphasizes the relevance of consumer values in real estate purchasing decisions, urging developers and marketers to prioritize ethical ideas, sustainable practices, and building a feeling of belonging and social connectedness. Offering eco-friendly amenities and green construction methods might attract clients, but creating a secure area for social interaction is critical. To the best of the authors’ knowledge, this research is the first to explore the role of egoistic and altruistic values on purchase intention, mainly in the housing and real estate sector, with the target consumers being young consumers in Canada. Full article

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation. Full article

This study evaluates the risk-adjusted performance of Hospitality REITs using multi-factor asset pricing models and downside risk measures with the aim of assessing their diversification potential and crisis sensitivity. Unlike prior studies that examine REITs in aggregate, this study isolates Hospitality REITs to explore their unique cyclical and macroeconomic sensitivities. This study looks at the risk-adjusted performance of Hospitality Real Estate Investment Trusts (REITs) in relation to more general REIT indexes and the S&P 500 Index. The study reveals that monthly returns of Hospitality REITs increasingly move in tandem with the stock markets during financial crises, which reduces their historical function as portfolio diversifiers. Investing in Hospitality REITs exposes one to the hospitality sector; however, these investments carry notable risks and provide little protection, particularly during economic upheavals. Furthermore, the study reveals that Hospitality REITs underperform on a risk-adjusted basis relative to benchmark indexes. The monthly returns of REITs show significant volatility during the post-COVID-19 era, which causes return-to-risk ratios to be below those of benchmark indexes. Estimates from multi-factor models indicate negative alpha values across conditional models, indicating that macroeconomic variables cause unremunerated risks. This industry shows great sensitivity to market beta and size and value determinants. Hospitality REITs’ susceptibility comes from their showing the most possibility for exceptional losses across asset classes under Value at Risk (VaR) and Conditional Value at Risk (CvaR) downside risk assessments. The findings have implications for investors and portfolio managers, suggesting that Hospitality REITs may not offer consistent diversification benefits during downturns but can serve a tactical role in procyclical investment strategies. Full article

In the context of increasing digitization, integrating ICT technologies, artificial intelligence, and remote working is altering residential mobility patterns and housing preferences. This study examines the housing market’s impact, focusing on how residential affordability affects residential choices, using a case study of the Metropolitan City of Florence. The analysis employs a methodology centered on the Debt-to-Income Ratio (DTI), which cross-references real estate market values (source: Agenzia delle Entrate and leading real estate portals) with household income brackets to identify affordable areas. The results reveal a clear divide: households with incomes below EUR 26,000 per year (representing about 69% of the population) are excluded from the central urban property market. This evidence confirms regional and national trends, emphasizing a growing mismatch between housing costs and disposable incomes. The study concludes that affordability is a technical–financial parameter and a valuable tool for supporting inclusive urban planning. Its application facilitates the orientation of effective public policies and the identification of socially sustainable housing solutions. Full article

The real estate market significantly influences individual lives, corporate decisions, and national economic sustainability. Therefore, constructing a data-driven, interpretable real estate market prediction model is essential. It can clarify each factor’s role in housing prices and transactions, offering a scientific basis for market regulation and enterprise investment decisions. This study comprehensively measures the evolution trends of the real estate markets in Beijing, Shanghai, Guangzhou, and Shenzhen, China, from 2003 to 2022 through three dimensions. Then, various machine learning methods and interpretability methods like SHAP values are used to explore the impact of supply, demand, policies, and expectations on the real estate market of China’s first-tier cities. The results reveal the following: (1) In terms of commercial housing sales area, adequate housing supply, robust medical services, and high population density boost the sales area, while demand for small units reflects buyers’ balance between affordability and education. (2) In terms of commercial housing average sales price, growth is driven by education investment, population density, and income, with loan interest rates serving as a stabilizing tool. (3) In terms of commercial housing sales amount, educational expenditure, general public budget expenditure, and real estate development investment amount drive revenue, while the five-year loan benchmark interest rate is the primary inhibitory factor. These findings highlight the divergent impacts of supply, demand, policy, and expectation factors across different market dimensions, offering critical insights for enterprise investment strategies. Full article

The world is characterised by the growing volumes and flow of goods, which, amid benefits to economic development, result in negative externalities affecting the sustainability of cities. Although numerous studies have analysed the locational patterns of logistics facilities in cities, further research is required to examine their real estate patterns and trends. The aim of the paper is, therefore, to analyse the value of land accommodating logistics facilities in the City of Cape Town municipality, South Africa. Given the lack of dedicated geo-spatial data, logistics firms were searched on Google Maps, utilising a combination of aerial photography and street view imagery. Three main attributes of land parcels hosting logistics facilities were thereafter captured from the municipal cadastral information: property extent, street address, and property number. The latter two were used to extract the 2018 and 2022 property market values from the valuation rolls on the municipal website, followed by statistical, spatial, and geographically weighted regression (GWR) analyses. Zones near the central business district and seaport, as well as areas with prime road-based accessibility, had high market values, while those near the railway stations did not stand out. However, GWR yielded weak relationships between market values and the locational variables analysed, arguably showing a disconnect between spatial planning and logistics planning. Towards augmenting sustainable logistics, it is recommended that relevant stakeholders strategically integrate logistics into spatial planning, and particularly revitalise freight rail to attract investment to logistics hubs with direct railway access. Full article

This study investigated the intangible value transferred from urban megaprojects to surrounding residential property markets, focusing on Brisbane’s transformative urban regeneration projects currently in the development pipeline. The research objectives were twofold: first, to empirically investigate the dynamics of property markets influenced by urban megaprojects and second, to assess the impact of a specific case study on these markets through a longitudinal analysis of residential sales data. Drawing from environmental economics, the concept of willingness to pay (WTP) is used to quantify externalities associated with urban megaprojects. The research constructs a comprehensive dataset integrating geospatial and property-specific data. Through revealed preference methods, the intangible value transferred from mixed-use developments is identified and quantified via residential transaction prices. Utilising hedonic price modelling, this study systematically analysed residential transaction data to estimate implicit prices associated with spatial proximity to megaprojects. A comprehensive dataset integrating property-specific attributes, geospatial proximity measures, and temporal dynamics of project development phases underpins this analysis. This research and its findings advance the existing literature in several important dimensions. That is, this research represents the first microeconomic assessment of the property market’s impacts resulting from mixed-use megaprojects in Brisbane, offering novel empirical insights for both academic and practical applications, how urban megaprojects shape residential property values, and informing stakeholders involved in urban planning, policymaking, and real estate investment decisions. Practitioners and policymakers can leverage these insights to inform policy frameworks and strategic decisions. At the governmental level, the results offer applicable insights for urban revitalisation strategies, particularly relevant to central business districts undergoing similar developments. Private sector stakeholders can utilise these outcomes to anticipate market adjustments, managing supply and demand fluctuations more effectively. Full article

With the growing global emphasis on sustainable development goals, Environmental, Social, and Governance (ESG) factors have emerged as critical considerations in shaping economic policies and strategies. This study employs the ARMA-eGARCH-skewed t and Vine Copula models, combined with the CoVaR method, to investigate the dependence structure and risk spillover pathways across various industrial sectors in China within the ESG framework. By modeling the complex interdependencies among sectors, this research uncovers the relationships between individual industries and the ESG benchmark index, while also analyzing the correlations across different sectors. Furthermore, this study quantifies the risk contagion effects across distinct industries under extreme market conditions and maps the pathways of risk spillovers. The findings highlight the pivotal role of ESG considerations in shaping industrial structures. Empirical results demonstrate that industries such as agriculture, energy, and manufacturing exhibit significant systemic risk characteristics in response to ESG fluctuations. Specifically, the identified risk spillover pathway follows the sequence: agriculture → consumption → ESG → manufacturing → energy. The CoVaR values for agriculture, energy, and manufacturing indicate a significant potential for risk contagion. Moreover, sectors such as real estate, finance, and information technology exhibit significant risk spillover effects. These findings offer valuable empirical evidence and a theoretical foundation for formulating ESG-related policies. This study suggests that effective risk management, promoting green finance, encouraging technological innovation, and optimizing industrial structures can significantly mitigate systemic risks. These measures can contribute to maintaining industrial stability and fostering sustainable economic development. Full article

Urban transformations require balancing private real estate interests with the provision of public spaces that enhance sustainability and ecosystem services. This study proposes a probabilistic model to assess the feasibility of transforming buildable areas while ensuring equitable benefits for both private developers and public administrations, with a focus on three areas to be regenerated within the Municipality of Lucca as case studies. Applying the Monte Carlo (MC) method, two probabilistic models—one with a Uniform distribution and the other with a Normal distribution—estimate the expected Transformation Value (TV) and its associated uncertainty. Results highlight the effectiveness of MC-based assessments in managing financial uncertainty, aiding developers in risk evaluation, and supporting policymakers in designing balanced urban planning indices. It was observed that the Uniform model is better suited to situations in which the initial values of the model’s main variables—such as construction costs, post-transformation market value, or transformation duration—are not fully known, whereas the Normal model provides more accurate estimates when the investment scenario is better understood. The results demonstrate that this approach provides, on the one hand, a robust tool for investment risk analysis to private investors and, on the other hand, a way for public institutions to verify whether urban planning indices enable private promoters to contribute effectively to the development of sustainable cities. Full article

Homebuilder ETFs provide investors with a diversified portfolio of residential construction and sales companies which reduces risks associated with individual stock selection in the sector. This study examines the net monthly returns of homebuilder exchange-traded funds (ETFs) through various performance evaluation models and market situations. The results reveal that these ETFs outperformed benchmark indices in absolute returns. Despite homebuilding being part of the real estate sector, the correlation between monthly returns of homebuilder ETFs and the Dow Jones US Real Estate Index, though positive, is not very high. The performance of ETFs varied across market conditions, demonstrating both outperformance and underperformance compared to U.S. stocks. During the COVID-19 pandemic, homebuilder ETFs displayed a decline, trailing behind U.S. equities in both absolute returns and risk-adjusted performance. This result emphasizes their vulnerability during economic crises. Utilizing a modified version of the Carhart factor model, significant exposure of real estate ETFs to the stock market was observed. Moreover, an assessment of ETF portfolio managers’ skills indicated proficiency in security selection but limited capabilities in market timing. Homebuilder ETFs pose higher downside risks than other indices, evident in their elevated Value at Risk (VaR) and Conditional Value at Risk (CVaR) values. Full article

Infrastructure and amenities have an evident effect on differentiated urban structures and house prices. However, few studies have taken into account the spatial heterogeneity of large-scale urban areas. Regarding this issue, the present study proposes a novel spatial framework to quantify the impacts of built environment factors on the housing market. We aim to answer: how does a specific factor impact house prices across different spatially autocorrelated neighbourhood clusters? The city of Shanghai, the economic centre of China, is examined through the transaction data from the China Real-estate Information Center (CRIC) are analysed. Firstly, spatially autocorrelation clusters were explored to identify high/low housing prices in concentrated areas in Shanghai. Secondly, using the development-scale house prices as the dependent variable, we employed ordinary least squares (OLS) linear regression and geographically weighted regression (GWR) models to examine the impact of built environment facilities on the house prices across these spatial autocorrelation regions and Shanghai more generally. The results suggest the following: (1) There are significant spatially autocorrelated clusters across Shanghai, with high-value clusters concentrated in the city core and low value concentrated in the suburban fringes; (2) Across Shanghai and its spatially autocorrelated clusters, transportation accessibility and service amenities factors can affect house prices quite differently, especially when focusing on the city centre and the suburban areas. Our results highlight the importance of optimising the city’s polycentric structural framework to foster a more balanced regional development. Differentiated approaches to the distribution of public service facilities should be adopted to address the diverse needs of residents across various regions. Full article

This study addresses the vast amount of information held by the banking sector, especially regarding opportunities in tourism development, production, and large residential projects. With advancements in information technology and databases, data mining has become essential for banks to optimally utilize available data. From January 2023 to July 2024, data from the Egyptian Arab Land Bank (EALB) were analyzed using data mining techniques, including rough set theory and the Weka version 3.0 program. The aim was to identify potential units for targeted marketing, improve customer satisfaction, and contribute to sustainable development goals. By integrating sustainability principles into financing approaches, this research promotes green banking, encouraging environmentally friendly and socially responsible investments. A survey of EALB customers assessed their interest in purchasing homes under the real estate financing program. The results were analyzed with GraphPad Prism version 9.0, with 95% confidence intervals and an R-squared value close to 1, and we identified 13 units (43% of the total units) as having the highest marketing potential. This study highlights data mining’s role in enhancing marketing for the EALB’s residential projects. Combining sustainable financing with data insights promotes green banking, aligning with customer preferences and boosting satisfaction and profitability. Full article

In the realm of real estate, each property owns a unique set of characteristics that distinguish it from others. While each property has its own distinctive features, the appraisal process prioritises only those qualities that meaningfully affect the value in the given market context. However, in the dynamically evolving market situation, expectations of real estate buyers can also transform. This study aims to explore how the surrounding environment and micro-location aspects affect the property value, which can deliver valuable outcomes for real estate market participants and researchers. For that purpose, the authors selected nine factors, called non-market attributes, that may affect the estimated value: air quality, noise emissions, green areas, rivers and water reservoirs, kindergartens and primary schools, universities, medical facilities, shopping centres and religious buildings. Moreover, apart from non-market attributes, the authors selected six market attributes usually used for the determination of residential real estate values according to the Polish regulations in this field. The detailed analysis of factors influencing the property value has been conducted based on the residential apartments in the district Zwięczyca in Rzeszów. Specifically, with the use of Pearson’s total correlation coefficients, authors explored market and non-market attributes and examined their relationships with unit transaction prices, attempting to answer the research question on whether non-market attributes can differentiate market values of residential apartments, when local real estate markets are considered. The results demonstrate that all selected market factors have a visible effect on analysed real estate prices and might be adopted for appraisal. Among nine non-market factors, only three of them have a pronounced effect on prices and might be used for the valuation of residential properties on the local market. The combined database of market and non-market factors reveals eight attributes (five market and three non-market) affecting prices of residential apartments. Full article

Understanding the determinants of housing price movements is an ongoing subject of debate. Estimating these determinants becomes a valuable tool for predicting price trends and mitigating the risks of market volatility. This article presents a systematic review analyzing studies that compare various machine learning (ML) tools with hedonic regression, aiming to assess whether real estate price predictions based on mathematical techniques and artificial intelligence enhance the accuracy of hedonic price models used for valuing residential properties. ML models (neural networks, decision trees, random forests, among others) provide high predictive capacity and greater explanatory power due to the better fit of their statistical measures. However, hedonic regression models, while less precise, are more robust, as they can identify the housing attributes that most influence price levels. These attributes include the property’s location, its internal features, and the distance from the property to city centers. Full article